Affiliations: [a] NeuroImaging and NeuroSpectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| [b]
Department of Neurology, All Indian Institute of Medical Sciences, New Delhi, India
| [c]
Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
Correspondence:
[*]
Correspondence to: Prof. Pravat K. Mandal and Dr. Deepika Shukla, NINS Laboratory, National Brain Research Centre, Gurgaon, Haryana, 122052, India. E-mail: [email protected]; E-mail: [email protected].
Note: [1] These authors contributed equally to this work.
Note: [2] Drs Mandal and Shukla dedicate this article in honor of their parents.
Abstract: Background:In vivo neuroimaging modalities such as magnetic resonance imaging (MRI), functional MRI (fMRI), magnetoencephalography (MEG), magnetic resonance spectroscopy (MRS), and quantitative susceptibility mapping (QSM) are useful techniques to understand brain anatomical structure, functional activity, source localization, neurochemical profiles, and tissue susceptibility respectively. Integrating unique and distinct information from these neuroimaging modalities will further help to enhance the understanding of complex neurological diseases. Objective:To develop a processing scheme for multimodal data integration in a seamless manner on healthy young population, thus establishing a generalized framework for various clinical conditions (e.g., Alzheimer’s disease). Methods:A multimodal data integration scheme has been developed to integrate the outcomes from multiple neuroimaging data (fMRI, MEG, MRS, and QSM) spatially. Furthermore, the entire scheme has been incorporated into a user-friendly toolbox- “PRATEEK”. Results:The proposed methodology and toolbox has been tested for viability among fourteen healthy young participants. The data-integration scheme was tested for bilateral occipital cortices as the regions of interest and can also be extended to other anatomical regions. Overlap percentage from each combination of two modalities (fMRI-MRS, MEG-MRS, fMRI-QSM, and fMRI-MEG) has been computed and also been qualitatively assessed for combinations of the three (MEG-MRS-QSM) and four (fMRI-MEG-MRS-QSM) modalities. Conclusion:This user-friendly toolbox minimizes the need of an expertise in handling different neuroimaging tools for processing and analyzing multimodal data. The proposed scheme will be beneficial for clinical studies where geometric information plays a crucial role for advance brain research.
Keywords: Multimodal integration, neuroimaging, region of interest, spatial overlap
